Machine Learning: An Algorithmic Perspective

Stephen Marsland

from $37.78

July 26, 2011 by Chapman and Hall/CRC
Textbook - 406 Pages - 168 B/W Illustrations
ISBN 9781439889213 - CAT# KE14585
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

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  • Provides a clear introduction to the basic concepts of machine learning
  • Focuses on algorithms and applications and uses explanation rather than equations and mathematical concepts
  • Presents real-world problems through structured exercises and programming examples
  • Contains a chapter that introduces the use of Python


Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.

Theory Backed up by Practical Examples

The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.

Highlights a Range of Disciplines and Applications

Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.


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